README
¶
TensorFlow in Go
Construct and execute TensorFlow graphs in Go.
WARNING: The API defined in this package is not stable and can change without notice. The same goes for the awkward package path (
github.com/tensorflow/tensorflow/tensorflow/go).
Quickstart
Refer to Installing TensorFlow for Go
Building the TensorFlow C library from source
If the "Quickstart" instructions above do not work (perhaps the release archives are not available for your operating system or architecture, or you're using a different version of CUDA/cuDNN), then the TensorFlow C library must be built from source.
Prerequisites
- bazel
- Environment to build TensorFlow from source code
(Linux
or OS
X).
If you don't need GPU support, then try the following:
sh # Linux sudo apt-get install python swig python-numpy # OS X with homebrew brew install swig
Build
-
Download the source code
go get -d github.com/tensorflow/tensorflow/tensorflow/go -
Build the TensorFlow C library:
cd ${GOPATH}/src/github.com/tensorflow/tensorflow ./configure bazel build --config opt //tensorflow:libtensorflow.soThis can take a while (tens of minutes, more if also building for GPU).
-
Make
libtensorflow.soavailable to the linker. This can be done by either:a. Copying it to a system location, e.g.,
sudo cp ${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow/libtensorflow.so /usr/local/libOR
b. Setting environment variables:
export LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow # Linux export LD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow # OS X export DYLD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow -
Build and test:
go test github.com/tensorflow/tensorflow/tensorflow/go
Generate wrapper functions for ops
Go functions corresponding to TensorFlow operations are generated in op/wrappers.go. To regenerate them:
Prerequisites:
- Protocol buffer compiler (protoc) 3.x
- The TensorFlow repository under GOPATH
go generate github.com/tensorflow/tensorflow/tensorflow/go/op
Support
Use stackoverflow and/or Github issues.
Contributions
Contributions are welcome. If making any signification changes, probably best to discuss on a Github issue before investing too much time. Github pull requests are used for contributions.
Documentation
¶
Overview ¶
Package tensorflow is a Go binding to TensorFlow.
The API is subject to change and may break at any time.
TensorFlow (www.tensorflow.org) is an open source software library for numerical computation using data flow graphs. This package provides functionality to build and execute such graphs and depends on TensorFlow being available. For installation instructions see https://www.tensorflow.org/code/tensorflow/go/README.md
Example ¶
package main
import (
"archive/zip"
"bufio"
"flag"
"fmt"
"io"
"io/ioutil"
"log"
"net/http"
"os"
"path/filepath"
tf "github.com/tensorflow/tensorflow/tensorflow/go"
"github.com/tensorflow/tensorflow/tensorflow/go/op"
)
func main() {
// An example for using the TensorFlow Go API for image recognition
// using a pre-trained inception model (http://arxiv.org/abs/1512.00567).
//
// Sample usage: <program> -dir=/tmp/modeldir -image=/path/to/some/jpeg
//
// The pre-trained model takes input in the form of a 4-dimensional
// tensor with shape [ BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, 3 ],
// where:
// - BATCH_SIZE allows for inference of multiple images in one pass through the graph
// - IMAGE_HEIGHT is the height of the images on which the model was trained
// - IMAGE_WIDTH is the width of the images on which the model was trained
// - 3 is the (R, G, B) values of the pixel colors represented as a float.
//
// And produces as output a vector with shape [ NUM_LABELS ].
// output[i] is the probability that the input image was recognized as
// having the i-th label.
//
// A separate file contains a list of string labels corresponding to the
// integer indices of the output.
//
// This example:
// - Loads the serialized representation of the pre-trained model into a Graph
// - Creates a Session to execute operations on the Graph
// - Converts an image file to a Tensor to provide as input to a Session run
// - Executes the Session and prints out the label with the highest probability
//
// To convert an image file to a Tensor suitable for input to the Inception model,
// this example:
// - Constructs another TensorFlow graph to normalize the image into a
// form suitable for the model (for example, resizing the image)
// - Creates and executes a Session to obtain a Tensor in this normalized form.
modeldir := flag.String("dir", "", "Directory containing the trained model files. The directory will be created and the model downloaded into it if necessary")
imagefile := flag.String("image", "", "Path of a JPEG-image to extract labels for")
flag.Parse()
if *modeldir == "" || *imagefile == "" {
flag.Usage()
return
}
// Load the serialized GraphDef from a file.
modelfile, labelsfile, err := modelFiles(*modeldir)
if err != nil {
log.Fatal(err)
}
model, err := ioutil.ReadFile(modelfile)
if err != nil {
log.Fatal(err)
}
// Construct an in-memory graph from the serialized form.
graph := tf.NewGraph()
if err := graph.Import(model, ""); err != nil {
log.Fatal(err)
}
// Create a session for inference over graph.
session, err := tf.NewSession(graph, nil)
if err != nil {
log.Fatal(err)
}
defer session.Close()
// Run inference on *imageFile.
// For multiple images, session.Run() can be called in a loop (and
// concurrently). Alternatively, images can be batched since the model
// accepts batches of image data as input.
tensor, err := makeTensorFromImage(*imagefile)
if err != nil {
log.Fatal(err)
}
output, err := session.Run(
map[tf.Output]*tf.Tensor{
graph.Operation("input").Output(0): tensor,
},
[]tf.Output{
graph.Operation("output").Output(0),
},
nil)
if err != nil {
log.Fatal(err)
}
// output[0].Value() is a vector containing probabilities of
// labels for each image in the "batch". The batch size was 1.
// Find the most probably label index.
probabilities := output[0].Value().([][]float32)[0]
printBestLabel(probabilities, labelsfile)
}
func printBestLabel(probabilities []float32, labelsFile string) {
bestIdx := 0
for i, p := range probabilities {
if p > probabilities[bestIdx] {
bestIdx = i
}
}
// Found the best match. Read the string from labelsFile, which
// contains one line per label.
file, err := os.Open(labelsFile)
if err != nil {
log.Fatal(err)
}
defer file.Close()
scanner := bufio.NewScanner(file)
var labels []string
for scanner.Scan() {
labels = append(labels, scanner.Text())
}
if err := scanner.Err(); err != nil {
log.Printf("ERROR: failed to read %s: %v", labelsFile, err)
}
fmt.Printf("BEST MATCH: (%2.0f%% likely) %s\n", probabilities[bestIdx]*100.0, labels[bestIdx])
}
// Convert the image in filename to a Tensor suitable as input to the Inception model.
func makeTensorFromImage(filename string) (*tf.Tensor, error) {
bytes, err := ioutil.ReadFile(filename)
if err != nil {
return nil, err
}
// DecodeJpeg uses a scalar String-valued tensor as input.
tensor, err := tf.NewTensor(string(bytes))
if err != nil {
return nil, err
}
// Construct a graph to normalize the image
graph, input, output, err := constructGraphToNormalizeImage()
if err != nil {
return nil, err
}
// Execute that graph to normalize this one image
session, err := tf.NewSession(graph, nil)
if err != nil {
return nil, err
}
defer session.Close()
normalized, err := session.Run(
map[tf.Output]*tf.Tensor{input: tensor},
[]tf.Output{output},
nil)
if err != nil {
return nil, err
}
return normalized[0], nil
}
// The inception model takes as input the image described by a Tensor in a very
// specific normalized format (a particular image size, shape of the input tensor,
// normalized pixel values etc.).
//
// This function constructs a graph of TensorFlow operations which takes as
// input a JPEG-encoded string and returns a tensor suitable as input to the
// inception model.
func constructGraphToNormalizeImage() (graph *tf.Graph, input, output tf.Output, err error) {
// Some constants specific to the pre-trained model at:
// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
//
// - The model was trained after with images scaled to 224x224 pixels.
// - The colors, represented as R, G, B in 1-byte each were converted to
// float using (value - Mean)/Scale.
const (
H, W = 224, 224
Mean = float32(117)
Scale = float32(1)
)
// - input is a String-Tensor, where the string the JPEG-encoded image.
// - The inception model takes a 4D tensor of shape
// [BatchSize, Height, Width, Colors=3], where each pixel is
// represented as a triplet of floats
// - Apply normalization on each pixel and use ExpandDims to make
// this single image be a "batch" of size 1 for ResizeBilinear.
s := op.NewScope()
input = op.Placeholder(s, tf.String)
output = op.Div(s,
op.Sub(s,
op.ResizeBilinear(s,
op.ExpandDims(s,
op.Cast(s,
op.DecodeJpeg(s, input, op.DecodeJpegChannels(3)), tf.Float),
op.Const(s.SubScope("make_batch"), int32(0))),
op.Const(s.SubScope("size"), []int32{H, W})),
op.Const(s.SubScope("mean"), Mean)),
op.Const(s.SubScope("scale"), Scale))
graph, err = s.Finalize()
return graph, input, output, err
}
func modelFiles(dir string) (modelfile, labelsfile string, err error) {
const URL = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"
var (
model = filepath.Join(dir, "tensorflow_inception_graph.pb")
labels = filepath.Join(dir, "imagenet_comp_graph_label_strings.txt")
zipfile = filepath.Join(dir, "inception5h.zip")
)
if filesExist(model, labels) == nil {
return model, labels, nil
}
log.Println("Did not find model in", dir, "downloading from", URL)
if err := os.MkdirAll(dir, 0755); err != nil {
return "", "", err
}
if err := download(URL, zipfile); err != nil {
return "", "", fmt.Errorf("failed to download %v - %v", URL, err)
}
if err := unzip(dir, zipfile); err != nil {
return "", "", fmt.Errorf("failed to extract contents from model archive: %v", err)
}
os.Remove(zipfile)
return model, labels, filesExist(model, labels)
}
func filesExist(files ...string) error {
for _, f := range files {
if _, err := os.Stat(f); err != nil {
return fmt.Errorf("unable to stat %s: %v", f, err)
}
}
return nil
}
func download(URL, filename string) error {
resp, err := http.Get(URL)
if err != nil {
return err
}
defer resp.Body.Close()
file, err := os.OpenFile(filename, os.O_RDWR|os.O_CREATE, 0644)
if err != nil {
return err
}
defer file.Close()
_, err = io.Copy(file, resp.Body)
return err
}
func unzip(dir, zipfile string) error {
r, err := zip.OpenReader(zipfile)
if err != nil {
return err
}
defer r.Close()
for _, f := range r.File {
src, err := f.Open()
if err != nil {
return err
}
log.Println("Extracting", f.Name)
dst, err := os.OpenFile(filepath.Join(dir, f.Name), os.O_WRONLY|os.O_CREATE, 0644)
if err != nil {
return err
}
if _, err := io.Copy(dst, src); err != nil {
return err
}
dst.Close()
}
return nil
}
Output:
Index ¶
Examples ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
Types ¶
type DataType ¶
type DataType C.TF_DataType
DataType holds the type for a scalar value. E.g., one slot in a tensor.
const ( Float DataType = C.TF_FLOAT Double DataType = C.TF_DOUBLE Int32 DataType = C.TF_INT32 Uint32 DataType = C.TF_UINT32 Uint8 DataType = C.TF_UINT8 Int16 DataType = C.TF_INT16 Int8 DataType = C.TF_INT8 String DataType = C.TF_STRING Complex64 DataType = C.TF_COMPLEX64 Complex DataType = C.TF_COMPLEX Int64 DataType = C.TF_INT64 Uint64 DataType = C.TF_UINT64 Bool DataType = C.TF_BOOL Qint8 DataType = C.TF_QINT8 Quint8 DataType = C.TF_QUINT8 Qint32 DataType = C.TF_QINT32 Bfloat16 DataType = C.TF_BFLOAT16 Qint16 DataType = C.TF_QINT16 Quint16 DataType = C.TF_QUINT16 Uint16 DataType = C.TF_UINT16 Complex128 DataType = C.TF_COMPLEX128 Half DataType = C.TF_HALF )
Types of scalar values in the TensorFlow type system.
type Graph ¶
type Graph struct {
// contains filtered or unexported fields
}
Graph represents a computation graph. Graphs may be shared between sessions.
func (*Graph) AddOperation ¶ added in v0.12.0
AddOperation adds an operation to g.
func (*Graph) Import ¶ added in v0.12.0
Import imports the nodes and edges from a serialized representation of another Graph into g.
Names of imported nodes will be prefixed with prefix.
func (*Graph) Operation ¶ added in v0.12.0
Operation returns the Operation named name in the Graph, or nil if no such operation is present.
func (*Graph) Operations ¶ added in v1.5.0
Operations returns a list of all operations in the graph
type Input ¶ added in v0.12.0
type Input interface {
// contains filtered or unexported methods
}
Input is the interface for specifying inputs to an operation being added to a Graph.
Operations can have multiple inputs, each of which could be either a tensor produced by another operation (an Output object), or a list of tensors produced by other operations (an OutputList). Thus, this interface is implemented by both Output and OutputList.
See OpSpec.Input for more information.
type OpSpec ¶ added in v0.12.0
type OpSpec struct {
// Type of the operation (e.g., "Add", "MatMul").
Type string
// Name by which the added operation will be referred to in the Graph.
// If omitted, defaults to Type.
Name string
// Inputs to this operation, which in turn must be outputs
// of other operations already added to the Graph.
//
// An operation may have multiple inputs with individual inputs being
// either a single tensor produced by another operation or a list of
// tensors produced by multiple operations. For example, the "Concat"
// operation takes two inputs: (1) the dimension along which to
// concatenate and (2) a list of tensors to concatenate. Thus, for
// Concat, len(Input) must be 2, with the first element being an Output
// and the second being an OutputList.
Input []Input
// Map from attribute name to its value that will be attached to this
// operation.
Attrs map[string]interface{}
}
OpSpec is the specification of an Operation to be added to a Graph (using Graph.AddOperation).
type Operation ¶
type Operation struct {
// contains filtered or unexported fields
}
Operation that has been added to the graph.
func (*Operation) NumOutputs ¶ added in v0.12.0
NumOutputs returns the number of outputs of op.
func (*Operation) OutputListSize ¶ added in v0.12.0
OutputListSize returns the size of the list of Outputs that is produced by a named output of op.
An Operation has multiple named outputs, each of which produces either a single tensor or a list of tensors. This method returns the size of the list of tensors for a specific output of the operation, identified by its name.
type Output ¶
type Output struct {
// Op is the Operation that produces this Output.
Op *Operation
// Index specifies the index of the output within the Operation.
Index int
}
Output represents one of the outputs of an operation in the graph. Has a DataType (and eventually a Shape). May be passed as an input argument to a function for adding operations to a graph, or to a Session's Run() method to fetch that output as a tensor.
type OutputList ¶ added in v0.12.0
type OutputList []Output
OutputList represents a list of Outputs that can be provided as input to another operation.
type PartialRun ¶ added in v1.1.0
type PartialRun struct {
// contains filtered or unexported fields
}
PartialRun enables incremental evaluation of graphs.
PartialRun allows the caller to pause the evaluation of a graph, run arbitrary code that depends on the intermediate computation of the graph, and then resume graph execution. The results of the arbitrary code can be fed into the graph when resuming execution. In contrast, Session.Run executes the graph to compute the requested fetches using the provided feeds and discards all intermediate state (e.g., value of intermediate tensors) when it returns.
For example, consider a graph for unsupervised training of a neural network model. PartialRun can be used to pause execution after the forward pass of the network, let the caller actuate the output (e.g., play a game, actuate a robot etc.), determine the error/loss and then feed this calculated loss when resuming the backward pass of the graph.
Example ¶
var (
// Create a graph: a + 2 + 3 + b.
//
// Skipping error handling for brevity of this example.
// The 'op' package can be used to make graph construction code
// with error handling more succinct.
g = NewGraph()
a, _ = Placeholder(g, "a", Int32)
b, _ = Placeholder(g, "b", Int32)
two, _ = Const(g, "Two", int32(2))
three, _ = Const(g, "Three", int32(3))
plus2, _ = Add(g, "plus2", a, two) // a + 2
plus3, _ = Add(g, "plus3", plus2, three) // (a + 2) + 3
plusB, _ = Add(g, "plusB", plus3, b) // ((a + 2) + 3) + b
)
sess, err := NewSession(g, nil)
if err != nil {
panic(err)
}
defer sess.Close()
// All the feeds, fetches and targets for subsequent PartialRun.Run
// calls must be provided at setup.
pr, err := sess.NewPartialRun(
[]Output{a, b},
[]Output{plus2, plusB},
[]*Operation{plus3.Op},
)
if err != nil {
panic(err)
}
// Feed 'a=1', fetch 'plus2', and compute (but do not fetch) 'plus3'.
// Imagine this to be the forward pass of unsupervised neural network
// training of a robot.
val, _ := NewTensor(int32(1))
fetches, err := pr.Run(
map[Output]*Tensor{a: val},
[]Output{plus2},
nil)
if err != nil {
panic(err)
}
v1 := fetches[0].Value().(int32)
// Now, feed 'b=4', fetch 'plusB=a+2+3+b'
// Imagine this to be the result of actuating the robot to determine
// the error produced by the current state of the neural network.
val, _ = NewTensor(int32(4))
fetches, err = pr.Run(
map[Output]*Tensor{b: val},
[]Output{plusB},
nil)
if err != nil {
panic(err)
}
v2 := fetches[0].Value().(int32)
fmt.Println(v1, v2)
Output: 3 10
type SavedModel ¶ added in v1.1.0
SavedModel represents the contents of loaded SavedModel. TODO(jhseu): Add and document metagraphdef when we pregenerate protobufs.
func LoadSavedModel ¶ added in v1.1.0
func LoadSavedModel(exportDir string, tags []string, options *SessionOptions) (*SavedModel, error)
LoadSavedModel creates a new SavedModel from a model previously exported to a directory on disk.
Exported models contain a set of graphs and, optionally, variable values. Tags in the model identify a single graph. LoadSavedModel initializes a session with the identified graph and with variables initialized to from the checkpoints on disk.
The tensorflow package currently does not have the ability to export a model to a directory from Go. This function thus currently targets loading models exported in other languages, such as using tf.saved_model.builder in Python. See: https://www.tensorflow.org/code/tensorflow/python/saved_model/
type Session ¶
type Session struct {
// contains filtered or unexported fields
}
Session drives a TensorFlow graph computation.
When a Session is created with a given target, a new Session object is bound to the universe of resources specified by that target. Those resources are available to this session to perform computation described in the GraphDef. After creating the session with a graph, the caller uses the Run() API to perform the computation and potentially fetch outputs as Tensors. A Session allows concurrent calls to Run().
func NewSession ¶
func NewSession(graph *Graph, options *SessionOptions) (*Session, error)
NewSession creates a new execution session with the associated graph. options may be nil to use the default options.
func (*Session) Close ¶
Close a session. This contacts any other processes associated with this session, if applicable. Blocks until all previous calls to Run have returned.
func (*Session) NewPartialRun ¶ added in v1.1.0
func (s *Session) NewPartialRun(feeds, fetches []Output, targets []*Operation) (*PartialRun, error)
NewPartialRun sets up the graph for incremental evaluation.
All values of feeds, fetches and targets that may be provided to Run calls on the returned PartialRun need to be provided to NewPartialRun.
See documentation for the PartialRun type.
func (*Session) Run ¶
func (s *Session) Run(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) ([]*Tensor, error)
Run the graph with the associated session starting with the supplied feeds to compute the value of the requested fetches. Runs, but does not return Tensors for operations specified in targets.
On success, returns the fetched Tensors in the same order as supplied in the fetches argument. If fetches is set to nil, the returned Tensor fetches is empty.
type SessionOptions ¶
type SessionOptions struct {
// Target indicates the TensorFlow runtime to connect to.
//
// If 'target' is empty or unspecified, the local TensorFlow runtime
// implementation will be used. Otherwise, the TensorFlow engine
// defined by 'target' will be used to perform all computations.
//
// "target" can be either a single entry or a comma separated list
// of entries. Each entry is a resolvable address of one of the
// following formats:
// local
// ip:port
// host:port
// ... other system-specific formats to identify tasks and jobs ...
//
// NOTE: at the moment 'local' maps to an in-process service-based
// runtime.
//
// Upon creation, a single session affines itself to one of the
// remote processes, with possible load balancing choices when the
// "target" resolves to a list of possible processes.
//
// If the session disconnects from the remote process during its
// lifetime, session calls may fail immediately.
Target string
// Config is a binary-serialized representation of the
// tensorflow.ConfigProto protocol message
// (https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto).
Config []byte
}
SessionOptions contains configuration information for a session.
type Shape ¶ added in v1.1.0
type Shape struct {
// contains filtered or unexported fields
}
Shape represents the (possibly partially known) shape of a tensor that will be produced by an operation.
The zero-value of a Shape represents a shape with an unknown number of dimensions.
func MakeShape ¶ added in v1.1.0
MakeShape returns a Shape with the provided size of each dimension.
A value of -1 implies that the size of the corresponding dimension is not known.
func ScalarShape ¶ added in v1.1.0
func ScalarShape() Shape
ScalarShape returns a Shape representing a scalar.
func (Shape) IsFullySpecified ¶ added in v1.1.0
IsFullySpecified returns true iff the size of all the dimensions of s are known.
func (Shape) NumDimensions ¶ added in v1.1.0
NumDimensions returns the number of dimensions represented by s, or -1 if unknown.
type Tensor ¶
type Tensor struct {
// contains filtered or unexported fields
}
Tensor holds a multi-dimensional array of elements of a single data type.
func NewTensor ¶
NewTensor converts from a Go value to a Tensor. Valid values are scalars, slices, and arrays. Every element of a slice must have the same length so that the resulting Tensor has a valid shape.
func ReadTensor ¶ added in v1.0.0
ReadTensor constructs a Tensor with the provided type and shape from the serialized tensor contents in r.
See also WriteContentsTo.
func (*Tensor) Value ¶
func (t *Tensor) Value() interface{}
Value converts the Tensor to a Go value. For now, not all Tensor types are supported, and this function may panic if it encounters an unsupported DataType.
The type of the output depends on the Tensor type and dimensions. For example: Tensor(int64, 0): int64 Tensor(float64, 3): [][][]float64
func (*Tensor) WriteContentsTo ¶ added in v1.0.0
WriteContentsTo writes the serialized contents of t to w.
Returns the number of bytes written. See ReadTensor for reconstructing a Tensor from the serialized form.
WARNING: WriteContentsTo is not comprehensive and will fail if t.DataType() is non-numeric (e.g., String). See https://github.com/tensorflow/tensorflow/issues/6003.
Source Files
¶
Directories
¶
| Path | Synopsis |
|---|---|
|
Command genop generates a Go source file with functions for TensorFlow ops.
|
Command genop generates a Go source file with functions for TensorFlow ops. |
|
internal
Package internal generates Go source code with functions for TensorFlow operations.
|
Package internal generates Go source code with functions for TensorFlow operations. |
|
Package op defines functions for adding TensorFlow operations to a Graph.
|
Package op defines functions for adding TensorFlow operations to a Graph. |